Long combination vehicles (LCVs) are modular combination vehicles that are longer and heavier than what
currently is allowed on European roads. These vehicle combinations have the potential to cut down overall
transportation costs, but also carbon dioxide emissions. Countries such as Canada and Australia already have
these truck combinations driving on their roads, and their use on European roads is expected to increase in the
near future. The LCVs however bring an undesired effect of increased difficulty of maneuvering on roads and in
traffic. Thus, their increased complexity calls for driver assisting systems. The development of these systems
leads to promising ways of improving traffic flow and increase the use of long combination trucks on current
roads.
In this thesis an existing framework for automated driving has been used which utilizes driver models for the
navigation of the LCV. The trajectories of the LCV are generated using numerical simulations of non-linear
ordinary differential equations (ODEs). The actuation requests, which are front wheel steering, propulsion and
braking are calculated using driver models. Up until now the parameters of the driver models have been fixed,
and were set by fitting data after an on-road study with professional truck drivers.
An approach for optimization of driver model parameters has been proposed in this thesis, which involves
genetic algorithms (GAs) and particle swarm optimization (PSO). In order to achieve a real-time feasible
implementation, the highly parallel nature of the GA and PSO are utilized. OpenCL was used as a platform to
implement the parallel processes for both algorithms which allowed for code execution on either CPU or GPU.
Optimzation of the driver model parameters showed that it could for a given dangerous scenario successfully
abort or complete a driving maneuver within given safety limits. The use of stochastic optimization proved to
be reliable and solutions were often found 100% of the time. As for the real time aspect of the optimization,
the results hinted that by lowering the number of iteration steps, optimizing code and upgrading the used
hardware, a real time implementation is within reach.

BibTeX @misc{Batkovic2016,author={Batkovic, Ivo},title={Optimization of driver model parameters for Long Combination Vehicles},abstract={Long combination vehicles (LCVs) are modular combination vehicles that are longer and heavier than what
currently is allowed on European roads. These vehicle combinations have the potential to cut down overall
transportation costs, but also carbon dioxide emissions. Countries such as Canada and Australia already have
these truck combinations driving on their roads, and their use on European roads is expected to increase in the
near future. The LCVs however bring an undesired effect of increased difficulty of maneuvering on roads and in
traffic. Thus, their increased complexity calls for driver assisting systems. The development of these systems
leads to promising ways of improving traffic flow and increase the use of long combination trucks on current
roads.
In this thesis an existing framework for automated driving has been used which utilizes driver models for the
navigation of the LCV. The trajectories of the LCV are generated using numerical simulations of non-linear
ordinary differential equations (ODEs). The actuation requests, which are front wheel steering, propulsion and
braking are calculated using driver models. Up until now the parameters of the driver models have been fixed,
and were set by fitting data after an on-road study with professional truck drivers.
An approach for optimization of driver model parameters has been proposed in this thesis, which involves
genetic algorithms (GAs) and particle swarm optimization (PSO). In order to achieve a real-time feasible
implementation, the highly parallel nature of the GA and PSO are utilized. OpenCL was used as a platform to
implement the parallel processes for both algorithms which allowed for code execution on either CPU or GPU.
Optimzation of the driver model parameters showed that it could for a given dangerous scenario successfully
abort or complete a driving maneuver within given safety limits. The use of stochastic optimization proved to
be reliable and solutions were often found 100% of the time. As for the real time aspect of the optimization,
the results hinted that by lowering the number of iteration steps, optimizing code and upgrading the used
hardware, a real time implementation is within reach.},publisher={Institutionen för tillämpad mekanik, Fordonsteknik och autonoma system, Chalmers tekniska högskola,},place={Göteborg},year={2016},series={Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, no: 2016:68},keywords={long combination vehicles, driver models, OpenCL, genetic algorithm, particle swarm optimization},}

RefWorks RT GenericSR ElectronicID 242688A1 Batkovic, IvoT1 Optimization of driver model parameters for Long Combination VehiclesYR 2016AB Long combination vehicles (LCVs) are modular combination vehicles that are longer and heavier than what
currently is allowed on European roads. These vehicle combinations have the potential to cut down overall
transportation costs, but also carbon dioxide emissions. Countries such as Canada and Australia already have
these truck combinations driving on their roads, and their use on European roads is expected to increase in the
near future. The LCVs however bring an undesired effect of increased difficulty of maneuvering on roads and in
traffic. Thus, their increased complexity calls for driver assisting systems. The development of these systems
leads to promising ways of improving traffic flow and increase the use of long combination trucks on current
roads.
In this thesis an existing framework for automated driving has been used which utilizes driver models for the
navigation of the LCV. The trajectories of the LCV are generated using numerical simulations of non-linear
ordinary differential equations (ODEs). The actuation requests, which are front wheel steering, propulsion and
braking are calculated using driver models. Up until now the parameters of the driver models have been fixed,
and were set by fitting data after an on-road study with professional truck drivers.
An approach for optimization of driver model parameters has been proposed in this thesis, which involves
genetic algorithms (GAs) and particle swarm optimization (PSO). In order to achieve a real-time feasible
implementation, the highly parallel nature of the GA and PSO are utilized. OpenCL was used as a platform to
implement the parallel processes for both algorithms which allowed for code execution on either CPU or GPU.
Optimzation of the driver model parameters showed that it could for a given dangerous scenario successfully
abort or complete a driving maneuver within given safety limits. The use of stochastic optimization proved to
be reliable and solutions were often found 100% of the time. As for the real time aspect of the optimization,
the results hinted that by lowering the number of iteration steps, optimizing code and upgrading the used
hardware, a real time implementation is within reach.PB Institutionen för tillämpad mekanik, Fordonsteknik och autonoma system, Chalmers tekniska högskola,T3 Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, no: 2016:68LA engLK http://publications.lib.chalmers.se/records/fulltext/242688/242688.pdfOL 30